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You're developing a multimodal A1 system that takes image data, text descriptions, and user interaction data (clicks, dwell time) to generate personalized product recommendations. To effectively combine these modalities and capture complex relationships, which model architecture would be most suitable?
Correct Answer: C
Deep learning architectures with attention mechanisms and cross-modal fusion layers are best suited for capturing complex relationships between different modalities. Attention mechanisms allow the model to focus on the most relevant features from each modality, while cross-modal fusion layers enable joint learning and prediction based on the combined representations. Linear regression, decision trees, KNN, and Naive Bayes are less capable of capturing complex, non-linear relationships in multimodal data.